Skew, Kurtosis, and Normality
Describing the shape of a distribution.
Skew, Kurtosis, and Normality is a free Data Science Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Data Science Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
The Shape of Data
Beyond the average, every column has a shape. Knowing whether it is symmetric, lopsided, or heavy-tailed changes how you analyze it. 📊
What Skew Means
Skewness measures how lopsided a distribution is. A perfectly symmetric bell has zero skew, while a long tail on one side pushes it positive or negative.
print(df["income"].skew())Right-Skewed Data
A positive skew has a long tail stretching right. Income is the classic case: most people cluster low while a few large values pull the mean upward.
Left-Skewed Data
A negative skew has its long tail on the left. Exam scores near a ceiling often look this way, with a few low results trailing far behind the pack.
Mean Versus Median
Skew separates the mean from the median. In right-skewed data the mean sits above the median, which is why median is the safer center for skewed columns.
What Kurtosis Means
Kurtosis describes the tails. High kurtosis means heavy tails with more extreme values, while low kurtosis means thin tails and fewer surprises.
print(df["income"].kurt())Heavy Tails, More Outliers
High kurtosis warns you to expect outliers. The distribution produces extreme values more often than a gentle bell curve ever would.
The Normal Distribution
The famous bell curve is the normal distribution. It is symmetric, has skew near zero, and many statistical methods quietly assume your data looks like it.
Checking Normality
You can check normality by eye with a histogram or a Q-Q plot, where points hugging the diagonal line suggest a roughly normal shape.
Why Normality Matters
Some tests and models assume normality. When data breaks that assumption, your p-values and confidence intervals can quietly become unreliable.
Fixing Skew
A heavy right skew often relaxes after a log transform. Reshaping the column toward normal can make later modeling steps behave far more nicely.
Quick Check
A column has a long tail to the right and a positive skew value. Which statement is true?
Recap
Skew shows lopsidedness and kurtosis shows tail heaviness. The normal curve is the symmetric baseline, and a log transform can tame stubborn skew. ✅
Frequently asked questions
Is the “Skew, Kurtosis, and Normality” lesson free?
Yes — the full text of “Skew, Kurtosis, and Normality” is free to read here on the web, and the Data Science Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Data Science Academy course, upgrade to CoddyKit PRO.
What will I learn in “Skew, Kurtosis, and Normality”?
Describing the shape of a distribution. You practise Data Science Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Data Science Academy?
No prior experience is required. Data Science Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Skew, Kurtosis, and Normality” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Data Science Academy lesson?
Yes. Every Data Science Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Correlation Is Not Causation
- Pearson vs Spearman
- Read a Correlation Heatmap
- Skew, Kurtosis, and Normality